Efficient Reasoning on Large SHIN Aboxes In Relational Databases

In this paper we consider knowledge bases that organize information using ontologies. Specifically, we investigate reasoning over a semantic web where the underlying knowledge base covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. In the semantic web over which we want to reason, frequent changes occur in the underlying knowledge base, and less frequent changes occur in the underlying ontology or the rule set that governs the reasoning. Interposing a backward chaining reasoner between a knowledge base and a query manager yields an architecture that can support reasoning in the face of frequent changes. However, such an interposition of the reasoning introduces uncertainty regarding the size and effort measurements typically exploited during query optimization. We present an algorithm for dynamic query optimization in such an architecture. We also introduce new optimization techniques to the backward-chaining algorithm. We show that these techniques together with the query-optimization reported on earlier, will allow us to actually outperform forward-chaining reasoners in scenarios where the knowledge base is subject to frequent change. Finally, we analyze the impact of these techniques on a large knowledge base that requires external storage.

Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, 2013

Ontology based knowledge management systems have a lot of potential: their applicability ranges from artificial intelligence, e.g., for knowledge representation and natural language processing, to information integration and retrieval systems, requirements analysis, and, most lately, to semantic web applications and workflow management systems. However the huge complexity of reasoning for ontologies with large TBoxes and/or ABoxes is often a barrier to their applicability in real-world settings especially those which are time sensitive. Materialization is a promising solution for scalable reasoning over ontologies with large ABoxes as it derives the implicit knowledge of an ontology and makes it available in a relational database. Although materialization can reduce the query answering time of an ontology, it has limitations in applications which require frequent update to the knowledge base. To overcome this problem, we developed a tool for incremental materialization which identifies the fragment of the ontology that needs to be updated due to the ABox or TBox change, thereby reducing the complexity of the exhaustive forward chaining required.

2008

ABSTRACT The growing popularity of semantic applications makes scalability of ontology reasoning tasks increasingly important. In this work, we firstly analyze the ontology landscape on the web, and identify typical clusters of expressivity. Secondly, we benchmark current ontology reasoners, by using representative ontologies for each cluster and a comprehensive set of queries. We point out applicability of specific reasoners to certain expressivity clusters.

2007

Abstract. We describe an approach to create a synthetic workload for large scale extensional query answering experiments. The workload comprises multiple interrelated domain ontologies, data sources which commit to these ontologies, synthetic queries and map ontologies that specify a graph over the domain ontologies.

2011

This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that succeed in classifying large ontologies expressed in the tractable OWL 2 EL profile.

2010

Initiatives like Linked Open Data have resulted in a rapid growth of the Web of data, and this growth is expected to continue. While impressive progress has been made in recent years in scalable storing, querying, and reasoning with languages like RDFS and OWL, existing reasoning techniques fail to perform when applied at Web-scale, due to the quantities of instance data, expressiveness of the ontologies, or the inherent inconsistency and incompleteness of data on the Web.

2012

Abstract. Ontology Based Data Access (OBDA) has drawn considerable attention from the OWL and RDF communities. In OBDA, instance data is accessed by means of mappings, which state the relationship between the data in a data source (eg, an RDBMSs) and the vocabulary of an ontology. In this paper we present Quest, a new system for OBDA focused on fast and efficient reasoning with large ontologies and large volumes of data.

W3C recommendation, publishing open and machine-readable content on the Web has recently received a lot more attention, including from corporate and governmental bodies; there now exists a rich vein of heterogeneous RDF data published on the Web (the so-called “Web of Data”) accessible to all.

2008

Abstract. HermiT is a new OWL reasoner based on a novel “hypertableau” calculus. The new calculus addresses performance problems due to nondeterminism and model size—the primary sources of complexity in state-of-the-art OWL reasoners. The latter is particularly important in practice, and it is achieved in HermiT with an improved blocking strategy and and an optimization that tries to reuse existing individuals rather than generating new ones.

2011

The goal of the Scalable OWL 2 Reasoning for Linked Data lecture is twofold: first, to introduce scalable reasoning and querying techniques to Semantic Web researchers as powerful tools to make use of Linked Data and large-scale ontologies, and second, to present interesting research problems for the Semantic Web that arise in dealing with TBox and ABox reasoning in OWL 2. The lecture consists of three parts.